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• Basic Research •     Next Articles

Prediction model of beef physiological maturity based on IGS-SVM

Fang fang JI 2,   

  • Received:2018-09-10 Revised:2019-06-26 Online:2019-08-15 Published:2019-08-26

Abstract: Abstract: Physiological maturity is an important indicator to determine the quality level of beef. This paper proposes a method to predict the physiological maturity of beef by optimizing the model of support vector machine (SVM) parameters through improved grid search (IGS) algorithm. A total of 25 beef samples of 18, 36, 54 and 72 months old were collected, totaling 100. Using machine vision, the microscopic images of the samples were collected. After image processing, the muscle fiber characteristic parameters of beef with different physiological maturity were extracted, and the correlation between the physiological maturity of beef and the characteristic parameters of muscle fibers was analyzed by statistical methods. Using the muscle fiber characteristic parameters as input, 76 training set samples were used to establish a SVM prediction model for beef physiological maturity. In order to optimize the SVM model which has been established, an improved grid search algorithm is proposed, which is used to optimize the constraint parameter C and kernel function parameter g of the SVM model. Combined with the leave-one-out cross validation method (LOO-CV), the optimal (C, g) parameter combination was obtained, and the optimal parameters were substituted into the classifier to obtain an optimized prediction model of beef physiological maturity. The applicability and estimation performance of the beef physiological maturity prediction model were tested with independent test samples of 24 test sets. The results showed that the accuracy of prediction of the physiological maturity of beef using the model could reach 91.67%. Compared with the traditional grid search algorithm (GS), the IGS algorithm reduces the model training time by 1781.3s. There is a significant correlation between beef muscle fiber characteristics and cattle age; according to the characteristic parameters of beef muscle fiber combined with machine vision technology, the physiological maturity of beef could be determined automatically.

Key words: Keywords:beef, physiological maturity, Support Vector Machine(SVM), prediction model

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